Segmentation and classification of offline hand drawn images for the BGT neuropsychological screening test

Moetesum, Momina; Siddiqi, Imran; Masroor, Uzma; Vincent, Nicole; Cloppet, Florence

doi:10.1117/12.2244139

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…We also implemented the shape context descriptor feature [44] employed in [27] for classification of same drawings. Using the same experimental protocol, shape context descriptor realizes a classification rate of 86%.…”

Section: B Resultsmentioning

confidence: 99%

“…Consequently, the drawings first need to be segmented before they can be fed to the recognition engine. The segmentation technique used in our study relies on Gestalt laws of Perception [25], [26] and is presented in detail in [27]. For completeness, we briefly outline the employed segmentation technique.…”

Section: A Segmentation Of Drawingsmentioning

confidence: 99%

“…For completeness, we briefly outline the employed segmentation technique. It can be observed from the BGT drawings (Figure 1-a) that the figures produced by subjects can be categorized into three classes [27] based on rules of Gestalt theory.…”

Section: A Segmentation Of Drawingsmentioning

confidence: 99%

“…Group B comprises drawings with solid lines which are distinguished from Group C using the foreground (drawing) area while Group C drawings are segmented using a k-means clustering on the spatial coordinates of the connected components. Complete details on the segmentation scheme can be found in [27].…”

Section: A Segmentation Of Drawingsmentioning

confidence: 99%

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Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Nazar¹,

Moetesum

Ehsan

et al. 2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes.

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Section: B Resultsmentioning

confidence: 99%

Section: A Segmentation Of Drawingsmentioning

confidence: 99%

Section: A Segmentation Of Drawingsmentioning

confidence: 99%

Section: A Segmentation Of Drawingsmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Nazar¹,

Moetesum

Ehsan

et al. 2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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Deformation modeling and classification using deep convolutional neural networks for computerized analysis of neuropsychological drawings

Moetesum

Siddiqi

Ehsan

et al. 2020

Neural Comput & Applic

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A survey of visual and procedural handwriting analysis for neuropsychological assessment

Moetesum

Diaz

Masroor

et al. 2022

Neural Comput & Applic

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To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed handwriting representation and estimation methods presented in the literature and discusses their strengths and weaknesses. It also highlights the limitations of existing processes and the challenges commonly faced when designing such systems. High-level directions for further research conclude the paper.

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Segmentation and classification of offline hand drawn images for the BGT neuropsychological screening test

Cited by 4 publications

References 4 publications

Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Deformation modeling and classification using deep convolutional neural networks for computerized analysis of neuropsychological drawings

A survey of visual and procedural handwriting analysis for neuropsychological assessment

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